class_docstr: Abstract base class for deterministic models.
  
  :meta private:
docstr: Compute the (deterministic) model output at X.
  
  Args:
      X: A `batch_shape x n x d`-dim input tensor `X`.
  
  Returns:
      A `batch_shape x n x m`-dimensional output tensor (the outcome
      dimension `m` must be explicit if `m=1`).
docstr_tok: ["Compute","the","(","deterministic",")","model","output","at","X.","Args",":","X",":","A","`","batch_shape","x","n","x","d","`","-dim","input","tensor","`","X","`",".","Returns",":","A","`","batch_shape","x","n","x","m","`","-dimensional","output","tensor","(","the","outcome","dimension","`","m","`","must","be","explicit","if","`","m=1","`",")","."]
code: def forward(self, X: Tensor) -> Tensor:
      r"""Compute the (deterministic) model output at X.
  
      Args:
          X: A `batch_shape x n x d`-dim input tensor `X`.
  
      Returns:
          A `batch_shape x n x m`-dimensional output tensor (the outcome
          dimension `m` must be explicit if `m=1`).
      """
      pass
  
imports: from __future__ import annotations
  from abc import abstractmethod
  from typing import Callable, List, Optional, Union
  import torch
  from botorch.models.ensemble import EnsembleModel
  from botorch.models.model import Model
  from torch import Tensor
label_desc: Use image node_id 1 for calling the DeterministicModel obj's underlying member method code with example usage: obj.forward(X) without return types